4 RELATIONSHIPS BETWEEN INDUSTRY .0 TECHNOLOGIES AND LEAN SOCIO-TECHNICAL PRACTICES
4.3 Relationships between Industry 4.0 and Lean (SLR)
Finally, the findings of the SLR and the cases study were synthesized demonstrating how I4T support Lean Socio-Technical practices and, and the impact of the relationships on economic, social, and environmental aspects. A conceptual framework was developed for both perspectives presented.
Often cited relationships also involve AR and BDA support for TPM and BDA support JIT (Table 4.4). IoT in conjunction with RFID, sensors and actuators can be used to support predictive maintenance (Chiarini and Kumar, 2020). The sensors enable to get information about vibration, noise, and heat, helping operators to detect abnormal conditions, identifying the most favorable moment to carry out maintenance (Satoglu et al., 2018). Machines can receive and send information to shop-floor and maintenance personnel about its production performance, indicating the need for maintenance to prevent future failures (Mora et al., 2017; Sanders et al., 2016). An intelligent information sharing and tracking system based on IoT gives accurate and timely information about the flows and materials throughout the supply chain, decreasing inaccuracies, and long lead times, which are vital to JIT performance (Zelbst et al., 2014). The use of IoT technologies in setup time reduction practices allows to reduce downtime whenever an operation change occurs (Tortorella et al., 2020), increasing the flexibility and productivity of production processes (Tortorella et al., 2019).
The IoT evolve Kanban pull system into an autonomous process over the IoT (Chiarini and Kumar, 2020), where data can be transmitted wirelessly to an inventory control system in real-time (Sanders et al., 2016). The AR instructs, train, support, and guide employees during TPM activities (Mora et al., 2017; Satoglu et al., 2018; Mayr et al., 2018), enabling activities to be carried out efficiently and at the correct frequency (Valamede and Akkari, 2020). BDA can be used to predict defects in equipements, which may increase the life expectancy of these instruments (Mayr et al., 2018; Valamede and Akkari, 2020). Big data enables be self-aware and self-maintained machines (Sanders et al., 2016). Although less numerous in the literature, there are several other supporting relationships between I4T and LTP presented in Table 4.5.
I4T supporting LTP improve economic performance indicators such as quality, flexibility, efficiency, productivity, costs reduction, reduced inventory, and reliability (Valamede and Akkari, 2020; Sanders et al., 2016), environmental performance indicators such as responsible use of resources (Núñez-Merino et al., 2020), and social performance indicators such as better working conditions and health of workers.
When analyzing the relationships considering the Lean social system, the gains for organizations are increased when there is integration between I4T and Lean Social Practices (LSP) (Ghobakhloo and Fathi, 2020). The strongest relationships found are related to I4.0 support for customer involvement, continuous improvement and supplier partnership, mainly IoT, followed by AR support for employee training (Table 4.4).
The use of IoT technologies supports the implementation of continuous improvement practices by facilitating the identification of errors in the system, enabling the capture, processing, sharing and forwarding of information to stakeholders, and allowing greater involvement of suppliers and customers (Haddud and Khare, 2020).
While the incorporation of the CPS provide more accurate data for decision-making in continuous improvement initiatives (Pagliosa et al., 2019). BDA incorporates several data analysis tools, which allows the identification of root causes more accurately and quickly, helping continuous improvement (Peças et al., 2021; Valamede and Akkari, 2020).
Several authors propose the use of AR to facilitate employee training (Koscielniak et al., 2019; Sordan et al., 2021). AR work together with employees, helping them in their manual tasks to avoid possible errors, besides presenting instructions and virtual elements that facilitate the training and performance of activities (Valamede and Akkari, 2020).
BDA, on the other hand, can give a better understanding of different customer segments' behavior and needs, enabling a proactive response to customer requirements (Núñez-Merino et al., 2020). An information-sharing structure based on Big Data strengthens customer involvement (Raut et al., 2019), which becomes even more relevant given the continuous feedback facilitated by the IoT.Despite the identification of a smaller number of citations about I4T support for LSPs, several other relationships were found (Table 4.6).
Some economic, environmental, and social benefits are reported from the integration between I4.0 and LSP. For example, waste and cost reduction and negative environmental externalities (Ghobadian et al., 2018), the employees may move into roles with less physical monotony and more intellectual stimulation (Sanders et al., 2016), enhanced human learning through intelligent assistance systems as well as human-machine interfaces that lead to increased employee satisfaction in industrial workplaces (Herrmann et al., 2014).
Table 4.5. How I4T support LTP
Relationships / How Sustainability
Performance
Authors (Year)
IoT / JIT
The application of IoT technologies provides real-time data on product locations and characteristics, which improves traceability and minimizes delays and waiting times, leading to more effective inventory management, and, consequently, reduced lead times.
IoT devices as sensors can detect the number of items in kanban baskets and automatically transmit the data to the control system. The system can automatically send orders to suppliers according to production line needs, which reduces stocks and frees up shop floor space.
Using IoT technologies, one process can trigger the production of another, introducing a perfect one-piece-flow pull system, which enables JIT.
IoT technologies: i) can send employees information if any product changes configuration or if the line balancing finds a new configuration.; ii) can display information corresponding to the precise product and phase of work on virtual work elements sheets; iii) can recognize different orders through barcode scanning devices and inform employees of the right components to assemble, which can dramatically reduce order preparation time. Thus, IoT accelerates work and avoids errors by strengthening JIT.
Economic Performance (quality, stock cost reduction, efficiency, productivity, costs reduction)
Anosike et al. (2021) Raji et al.
(2021) Chiarini and Kumar (2020) Ciano et al.
(2020) Bittencourt et al. (2019) Mayr et al.
(2018) Valamede and Akkari (2020) Zelbst et al.
(2014)
IoT / Continuous flow
Continuous flow seeks to establish a simplified flow of products without major stops throughout the company (Sanders et al., 2016). In this context, sensors can monitor production volumes and help companies reduce unfinished work in the process and resolve issues created by task switching and order reprioritizing, which allows for workflow improvements.
Ghobakhloo and Fathi (2020) Ciano et al.
(2020) Raji et al.
(2021)
IoT / Kanban
IoT technologies can monitor kanban schedule changes and the charge level of the box, and the data can be automatically transmitted to a real-time inventory control system. Furthermore, real-time process monitoring allows minimum batch quantity to be set per workstation. When the workstation reaches minimum stock the information is displayed on the predecessor workstation to forward the material.
Kumar et al.
(2018) Sanders et al. (2016)
IoT / TPM
An IoT maintenance system allows a quick response to failures. The analysis of collected data can link the occurred failure with past patterns and causes, which can prevent potential failures. In addition, it is possible to request repairs and order spare pieces automatically. Advanced sensors can measure parameters in machines such as times, speed, pressures, vibrations, temperatures, etc.
Anosike et al. (2021) Chiarini and Kumar (2020) Ghobakhloo and Fathi (2020) Tortorella et al. (2021) Raji et al.
(2021) Sordan et al.
(2021)
Table 4.5. (Continued)
Relationships / How Sustainability
Performance
Authors (Year)
IoT / Setup time reduction
Machines-embedded learning enabled by IoT technologies can support setup time reduction practices since machines can perform accurate Single-Minute Exchange of Dies (SMED) procedures followed consistently.
IoT technologies can receive material, product, or work phase information and prepare the correct configuration of the machines.
Sensors or RFID can recognize the right tool for the right machine or set the parameters corresponding to the new production cycle which results in a faster change of machine parameters according to the instructions read on the piece, reducing setup time.
Anosike et al. (2021) Ciano et al.
(2020) Ciano et al.
(2020) Sordan et al.
(2021) Sanders et al. (2016)
IoT / SPC
IoT and Smart sensors for collecting data linked to characteristics of the process permit SPC with autonomous feedback to the machine in case of deviation from the limits and unlikely patterns in the data appear. In addition, communication can be done in real-time through supervisors’ smartphones.
Chiarini and Kumar (2020) Sordan et al.
(2021)
BDA / JIT
Data shared in the cloud between supply chain partners can be processed by BDA, which significantly reduces order execution time, can identify trends and assists in immediate decision-making. Moreover, it helps analyze demand trends, for example, during peak seasons, for proper forecasting and planning purposes, avoiding delays.
Economic Performance (quality, efficiency, stock cost reduction, flexibility)
Raji et al.
(2021) Mayr et al.
(2018) Valamede and Akkari (2020)
BDA / Kanban
BDA increases the transparency of material and process movements and enables the combination of target and actual values to remove excess inventories.
Valamede and Akkari (2020)
BDA / TPM
BDA can analyze machine problems and anticipate potential breakdown and identify the root cause. Thus, Big Data capabilities allow to improve preventive maintenance routines, identifying patterns to optimize component life based on current usage conditions.
Li (2019) Tortorella et al. (2021) Sanders et al. (2016)
CPS / JIT
CPS can control when the material stock reaches the minimum level and automatically generate a purchase order for the supplier.
Economic Performance (quality, efficiency, stock cost reduction, flexibility, reliability, productivity)
Santos et al.
(2021)
CPS / Continuous flow
CPS allows the identification of cycle times to find the best solution between the highest possible capacity utilization per working station and a continuous flow of production.
Kolberg and Zuhlke (2015)
CPS / Kanban
CPS integrated into the workstations can directly control the production process through the connected actuators and update data automatically. If the stock at a workstation drops under the reorder level, the CPS automatically sends a Kanban to the predecessor, operating on the principle of self-regulation Decentralized data collection and transfer allows full visibility of all processes that are part of the Kanban system.
Kolberg et al. (2016) Pekarcikova et al. (2020)
CPS / Setup time reduction
CPS provides a flexible and modular production through its computing capacity and connectable sensors. Working stations or whole production lines can be efficiently reconfigured, significantly reducing setup time.
Kolberg and Zuhlke (2015)
Table 4.5. (Continued)
Relationships / How Sustainability
Performance
Authors (Year)
CPS / SPC
CPS can combine historical running data of the system to analyze abnormalities on the basis of mature data statistical analyses and mining algorithms. Moreover, define knowledge rules to predict possible system anomalies and sending pre-alarms production process based on SPC.
Ma et al.
(2017)
AGV / Continuous flow
The AGVs: i) allow autonomous control in operational functions and promote continuous flow; ii) identify routes and components for specific products, avoiding downtime and improving production flow by adding a sequential component to work in progress.
Economic Performance (quality, flexibility, productivity, stock cost reduction)
Ciano et al.
(2020) Núñez-Merino et al.
(2020) Bittencourt et al. (2019) Mayr et al.
(2018)
AGV / JIT
The AGV can automatically supply in-process stocks by transporting materials in the exact amount and time they are requested, minimizing stocks and favoring JIT.
Mayr et al.
(2018) Núñez-Merino et al.
(2020) Valamede and Akkari (2020) AGV / Kanban
The AGV performs a coordinated supply of kanban boxes in order to avoid excess and lack of materials.
Núñez-Merino et al.
(2020) Valamed and Akkari (2020) AR / JIT
AR devices can provide individualized instructions about the tasks required to run and bring information about cycle times into the visual field of employees, supporting JIT processing.
Economic Performance (quality, flexibility), Environmental Performance (reduction of environmental impacts, reduction pollution, reduction use of natural resources), and Social Performance (improvement of working conditions, reduction of the number of accidents at work)
Kolberg and Zuhlke (2015)
Ma et al.
(2017)
AR / TPM
AR devices provide employees with precise instructions to routine maintenance or faulty components/items.
Raji et al.
(2021) Koscielniak et al. (2019) AR / Continuous flow
AR provides cycle times information in the visual field of workers, allowing continuous production flow.
Valamede and Akkari (2020)
Table 4.5. (Continued)
Relationships / How Sustainability
Performance
Authors (Year)
AM / JIT
AM can offer shorter lead times and reduced inventory, can reduce the time required for tooling and retooling operations and enables the production of pieces and products close to the point of use by further decentralizing and redistributing manufacturing. AM technologies can meet exact customer requests using less raw material and process time as it produces just the amount needed with flexibility when adding layers of material.
Economic Performance (production cost reduction, flexibility)
Raji et al.
(2021) Ghobadian et al. (2018) Valamede and Akkari (2020)
AM / Setup time reduction
AM equipment can produce a different object by altering the software. Thus, AM can produce varied workpieces with short setup times, i.e., selection, search, and
adjustment times for tools and workpieces are technologically reduced to a minimum.
Ghobadian et al. (2018) Mayr et al.
(2018)
Simulation / JIT
Process simulation allow the visualization of the simulated environment to test if the layout configuration promotes a continuous flow.
Economic Performance (stock cost reduction, productivity)
Ciano et al.
(2020)
Simulation / TPM
Digital Twin Simulation allow the application of different maintenance solutions and the prediction of future maintenance.
Ciano et al.
(2020)
Simulation/ Kanban
The simulation ensures the identification of optimal kanban parameters such as lot size, inventory, or delivery frequency. When external changes are required, the system updates the parameters autonomously.
Mayr et al.
(2018) Pekarcikova et al. (2020)
CC / Kanban
The cloud-based Kanban system has features for entering production data (e.g., number of machines available, number of employees, raw material availability) and a decision support system simulation. Factors such as labor hours, number of bad quality products, and production hours lost due to downtime are also entered to estimate the kanban ideals parameters.
Economic Performance (quality, efficiency, stock cost reduction, flexibility)
Shahin et al.
(2020)
Table 4.6. How I4T support LSP
Relationships / How Sustainability
Performance
Authors (Year)
IoT / Continuous improvement
IoT enables the data collection from machines where a machine learning algorithm can be used in conjunction with a problem-solving database to infer cause-problem relationships, which helps continuous improvement teams. The collected data from IoT can feed a system of indicators to promote continuous improvement projects aiming at more efficient use of resources and energy. Furthermore, IoT sensors on smart products can collect process data during and after production. Thus, it is possible to automatically gather information individualized by product and production line assisting in continuous improvement projects.
Economic Performance (quality, costs reduction, efficiency, productivity) and
Environmental Performance (reduction of environmental impacts, reduction of energy consumption)
Peças et al.
(2021) Haddud and Khare (2020) Ferrera et al. (2017) Kolberg and Zuhlke (2015) IoT / Customer involvement
Products purchased can be registered using a QR Code to allow the companies to immediately capture user information, establishing greater proximity to customers’
needs.
Li (2019) Sensors and IoT technologies can be applied in products to transform them into smart
products (integrated with devices that track usage data and send for smart factories) allowing continuous feedback and usage analysis to understand and serve customers better.
Sanders et al. (2016)
IoT / Supplier partnership
QR codes can be used for components delivered by suppliers, which will make delivery information clearer, plan changes easier to match, and reduce inventory.
Ciano et al.
(2020) Li (2019) BDA / Continuous improvement
Analytics such as machine learning, data mining, root cause analysis, correlation analysis, and predictive analysis performed by BDA contributes to the process of continuous improvement by improving data analysis. Since, the use of advanced analysis tools capable of dealing with a large volume of data automatically collected, for example, from sensors overcomes the limitations of simpler analysis tools.
Furthermore, data from stakeholders are collected through IoT devices and shared in a cloud computing environment with speed and variability. These data are processed by BDA and can be used in continuous improvement initiatives.
Economic Performance (quality, efficiency), Environmental Performance (reduction of energy consumption, reduction of water consumption, reduced industrial waste, efficient use of resources)
Peças et al.
(2021) Valamede and Akkari (2020) BDA / Customer involvement
Using specific algorithms, the BDA can filter and use a large amount of data from customers, including their requirements (voice of the customer) and perceptions about products and services, strengthening customer involvement strategies.
Sordan et al. (2021) Núñez-Merino et al. (2020) Raut et al.
(2019) CPS / Continuous improvement
CPS components on the workstation can provide historical data for fault analysis and continuous improvement processes. The digital information obtained by the physical system can generate a problem-solving mechanism by creating co-creative platforms that guide continuous improvement strategies.
Economic Performance (quality, productivity, efficiency)
Li (2019) Kolberg et al. (2016) CPS / Customer involvement
CPS can transfer customer experience knowledge effectively linking this information to organizational capabilities and solving various problems at the manufacturing site autonomously and flexibly.
Li (2019)
Table 4.6. (Continued)
Relationships / How Sustainability
Performance
Authors (Year) AR / Training employees
AR replaces traditional communication of operational standards on paper. Through tablets, head-mounted displays and three-dimensional holograms, AR can improve training employees' activities, what can decrease time the time to acquire the knowledge. The use of AR in training activities provides more in-depth knowledge about production processes. Thus, employees are empowered to find possible solutions to critical problems.
Sordan et al. (2021) Social
Performance (improvement of worker health)
Koscielniak et al.
(2019) Valamede and Akkari (2020) AM / Customer involvement
There is greater customer involvement through the analysis of data that can be provided by using smart products and more easily customized products, through AM and the flexibility allowed by technologies, and a better understanding of customer’s requirements.
Hadud and Kare (2020) Núñez-Merino et al. (2020) Simulation / Continuous improvement
Simulated models can be used to test improvements in the production system, evaluating their impact in a virtual environment.
Peças et al.
(2021)